Multiclass Fuzzy Time-Delay Common Spatio-Spectral Patterns with Fuzzy Information Theoretic Optimization for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

© 2019 IEEE. Electroencephalogram (EEG) signals are one of the most widely used noninvasive signals in brain-computer interfaces. Large dimensional EEG recordings suffer from poor signal-to-noise ratio. These signals are very much prone to artifacts and noise, so sufficient preprocessing is done on raw EEG signals before using them for classification or regression. Properly selected spatial filters enhance the signal quality and subsequently improve the rate and accuracy of classifiers, but their applicability to solve regression problems is quite an unexplored objective. This paper extends common spatial patterns (CSP) to EEG state space using fuzzy time delay and thereby proposes a novel approach for spatial filtering. The approach also employs a novel fuzzy information theoretic framework for filter selection. Experimental performance on EEG-based reaction time (RT) prediction from a lane-keeping task data from 12 subjects demonstrated that the proposed spatial filters can significantly increase the EEG signal quality. A comparison based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation to true responses is made for all the subjects. In comparison to the baseline fuzzy CSP regression one versus rest, the proposed Fuzzy Time-delay Common Spatio-Spectral filters reduced the RMSE on an average by 9.94%, increased the correlation to true RT on an average by 7.38%, and reduced the MAPE by 7.09%

Coordinated Regulation of ATF2 by miR-26b in γ-Irradiated Lung Cancer Cells

MicroRNA regulates cellular responses to ionizing radiation (IR) through translational control of target genes. We analyzed time-series changes in microRNA expression following γ-irradiation in H1299 lung cancer cells using microarray analysis. Significantly changed IR-responsive microRNAs were selected based on analysis of variance analysis, and predicted target mRNAs were enriched in mitogen-activated protein kinase (MAPK) signaling. Concurrent analysis of time-series mRNA and microRNA profiles uncovered that expression of miR-26b was down regulated, and its target activating transcription factor 2 (ATF2) mRNA was up regulated in γ-irradiated H1299 cells. IR in miR-26b overexpressed H1299 cells could not induce expression of ATF2. When c-Jun N-terminal kinase activity was inhibited using SP600125, expression of miR-26b was induced following γ-irradiation in H1299 cells. From these results, we concluded that IR-induced up-regulation of ATF2 was coordinately enhanced by suppression of miR-26b in lung cancer cells, which may enhance the effect of IR in the MAPK signaling pathway

Arya: Nearly linear-time zero-knowledge proofs for correct program execution

There have been tremendous advances in reducing interaction, communication and verification time in zero-knowledge proofs but it remains an important challenge to make the prover efficient. We construct the first zero-knowledge proof of knowledge for the correct execution of a program on public and private inputs where the prover computation is nearly linear time. This saves a polylogarithmic factor in asymptotic performance compared to current state of the art proof systems. We use the TinyRAM model to capture general purpose processor computation. An instance consists of a TinyRAM program and public inputs. The witness consists of additional private inputs to the program. The prover can use our proof system to convince the verifier that the program terminates with the intended answer within given time and memory bounds. Our proof system has perfect completeness, statistical special honest verifier zero-knowledge, and computational knowledge soundness assuming linear-time computable collision-resistant hash functions exist. The main advantage of our new proof system is asymptotically efficient prover computation. The prover’s running time is only a superconstant factor larger than the program’s running time in an apples-to-apples comparison where the prover uses the same TinyRAM model. Our proof system is also efficient on the other performance parameters; the verifier’s running time and the communication are sublinear in the execution time of the program and we only use a log-logarithmic number of rounds

The Accumulation of Organic Carbon in Mineral Soils by Afforestation of Abandoned Farmland

The afforestation of abandoned farmland significantly influences soil organic carbon (OC). However, the dynamics between OC inputs after afforestation and the original OC are not well understood. To learn more about soil OC dynamics after afforestation of farmland, we measured the soil OC content in paired forest and farmland plots in Shaanxi Province, China. The forest plots had been established on farmland 18, 24, 48, 100, and 200 yr previously. The natural 13C abundance of soil organic matter was also analyzed to distinguish between crop- and forest-derived C in the afforested soils. We observed a nonlinear accumulation of total OC in the 0–80 cm depth of the mineral soil across time. Total soil OC accumulated more rapidly under forest stands aged 18 to 48 yr than under forest stands aged 100 or 200 yrs. The rate of OC accumulation was also greater in the 0–10 cm depth than in the 10–80 cm depth. Forest-derived OC in afforested soils also accumulated nonlinearly across time, with the greatest increase in the 0–20 cm depth. Forest-derived OC in afforest soils accounted for 52–86% of the total OC in the 0–10 cm depth, 36–61% of the total OC in the 10–20 cm depth, and 11–50% of the total OC in the 20–80 cm depth. Crop-derived OC concentrations in the 0–20 cm depth decreased slightly after afforestation, but there was no change in crop-derived OC concentrations in the 20–80 cm depth. The results of our study support the claim that afforestation of farmland can sequester atmospheric CO2 by increasing soil OC stocks. Changes in the OC stocks of mineral soils after afforestation appear to be influenced mainly by the input of forest-derived C rather than by the loss of original OC

Constructing the HBV-human protein interaction network to understand the relationship between HBV and hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>Epidemiological studies have clearly validated the association between hepatitis B virus (HBV) infection and hepatocellular carcinoma (HCC). Patients with chronic HBV infection are at increased risk of HCC, in particular those with active liver disease and cirrhosis.</p> <p>Methods</p> <p>We catalogued all published interactions between HBV and human proteins, identifying 250 descriptions of HBV and human protein interactions and 146 unique human proteins that interact with HBV proteins by text mining.</p> <p>Results</p> <p>Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HBV are made up of core proteins that are interconnected with many pathways. A global analysis based on functional annotation highlighted the enrichment of cellular pathways targeted by HBV.</p> <p>Conclusions</p> <p>By connecting the cellular proteins targeted by HBV, we have constructed a central network of proteins associated with hepatocellular carcinoma, which might be to regard as the basis of a detailed map for tracking new cellular interactions, and guiding future investigations.</p

Hospital Readmission in General Medicine Patients: A Prediction Model

Background: Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models. Objective: To identify predictors of early hospital readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk. Design: Prospective observational cohort study. Patients: Participants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts. Measurements: We identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk. Results: Approximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, ≥1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of ≥25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively. Conclusions: Select patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission

Complement C1 Esterase Inhibitor Levels Linked to Infections and Contaminated Heparin-Associated Adverse Events

Activation of kinin-kallikrein and complement pathways by oversulfated-chondroitin-sulfate (OSCS) has been linked with recent heparin-associated adverse clinical events. Given the fact that the majority of patients who received contaminated heparin did not experience an adverse event, it is of particular importance to determine the circumstances that increase the risk of a clinical reaction. In this study, we demonstrated by both the addition and affinity depletion of C1inh from normal human plasma, that the level of C1inh in the plasma has a great impact on the OSCS-induced kallikrein activity and its kinetics. OSCS-induced kallikrein activity was dramatically increased after C1inh was depleted, while the addition of C1inh completely attenuated kallikrein activity. In addition, actual clinical infection can lead to increased C1inh levels. Plasma from patients with sepsis had higher average levels of functional C1inh and decreased OSCS-induced kallikrein activity. Lastly, descriptive data on adverse event reports suggest cases likely to be associated with contaminated heparin are inversely correlated with infection. Our data suggest that low C1inh levels can be a risk factor and high levels can be protective. The identification of risk factors for contact system-mediated adverse events may allow for patient screening and clinical development of prophylaxis and treatments

Low atmospheric CO2 levels during the Little Ice Age due to cooling-induced terrestrial uptake

Low atmospheric carbon dioxide (CO2) concentration during the Little Ice Age has been used to derive the global carbon cycle sensitivity to temperature. Recent evidence confirms earlier indications that the low CO2 was caused by increased terrestrial carbon storage. It remains unknown whether the terrestrial biosphere responded to temperature variations, or there was vegetation re-growth on abandoned farmland. Here we present a global numerical simulation of atmospheric carbonyl sulfide concentrations in the pre-industrial period. Carbonyl sulfide concentration is linked to changes in gross primary production and shows a positive anomaly during the Little Ice Age. We show that a decrease in gross primary production and a larger decrease in ecosystem respiration is the most likely explanation for the decrease in atmospheric CO2 and increase in atmospheric carbonyl sulfide concentrations. Therefore, temperature change, not vegetation re-growth, was the main cause of the increased terrestrial carbon storage. We address the inconsistency between ice-core CO2 records from different sites measuring CO2 and δ13CO2 in ice from Dronning Maud Land (Antarctica). Our interpretation allows us to derive the temperature sensitivity of pre-industrial CO2 fluxes for the terrestrial biosphere (γL = -10 to -90 Pg C K-1), implying a positive climate feedback and providing a benchmark to reduce model uncertainties

Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data